United States
Environmental Protection 0
Agency September 2014
Assessing Hydrologic
Impacts of Future Land
Cover Change Scenarios
in the South Platte River
Basin (CO, WY, & NE)
RESEARCH AND DEVELOPMENT
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Assessing Hydrologic Impacts
of Future Land Cover Change
Scenarios in the South Platte
River Basin (CO, WY, & NE)
J. E. Barlow1, I.S. Burns1, W.G. Kepner2, G.S. Sidman1, D.C. Goodrich3,
DP. Guertin1, and J.M. McCarthy4
1University of Arizona, School of Natural Resources and the Environment, Tucson, AZ
2U.S. Environmental Protection Agency, Office of Research and Development, Las Vegas, NV
3USDA-Agricultural Research Service, Southwest Watershed Research Center, Tucson, AZ
4U.S. Environmental Protection Agency, Region 8, Denver, CO
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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Acknowledgements
This project was funded through the U.S. Environmental Protection Agency (EPA) Regional
Applied Research Effort (RARE) Program, which is administered by the Office of Research and
Development's (ORD) Regional Science Program.
We would like to acknowledge the key reviewers of this report for their valuable suggestions.
Specifically, our thanks go to Dr. W. Paul Miller, Senior Hydrologist, National Oceanic and
Atmospheric Administration (NOAA), Colorado Basin River Forecast Center, Salt Lake City,
UT; Dr. Britta G. Bierwagen, EPA/ORD, Global Change Research Program, National Center for
Environmental Assessment, Washington, DC; and Dr. Francina Dominguez, Assistant Professor
in Department of Atmospheric Sciences and co-Director of Hydrometeorology Program,
University of Arizona, Tucson, AZ.
This report has been subjected to the EPA/ORD and U.S. Department of
Agriculture/Agricultural Research Service (USDA/ARS) peer and administrative review
processes and has been approved for publication. The Automated Geospatial Watershed
Assessment (AGWA) tool was jointly developed by EPA/ORD, USDA/ARS, and the University
of Arizona. The Integrated Climate and Land Use Scenarios (ICLUS) database was developed by
EPA/ORD. AGWA and ICLUS are endorsed and recommended by each of the respective
agencies, especially in regard to their integrated use.
in
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IV
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Table of Contents
Acknowledgements iii
Table of Contents v
List of Figures vii
List of Tables ix
Acronyms and Abbreviations xii
Abstract 1
Introduction 1
Methods 4
Project/Watershed Extent 4
Land Cover 4
Soils 8
Precipitation 8
Reservoirs 9
AGWA-SWAT Modeling 10
Results 11
Discussion 23
Conclusions 25
Appendix A 29
Appendix B 30
Appendix C 32
Appendix D 35
References 41
v
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VI
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List of Figures
Figure 1: Location map of the study area with CFSR points for precipitation and
weather generating stations for the South Platte River Basin 3
Figure 2: Population projections for ICLUS scenarios by decade (EPA 2009) 7
Figure 3: Population projections for ICLUS scenarios by decade for counties that
intersect the South Platte River Basin (ORD 2007; Appendix B - Figure 21) 7
Figure 4: Watershed Human Use Index (HUI) for all scenarios 11
Figure 5: Watershed average percent change in average annual surface runoff for all
scenarios 12
Figure 6: Watershed average percent change in average annual sediment yield for all
scenarios 12
Figure 7: Subwatershed #340 Human Use Index (HUI) for all scenarios 13
Figure 8: Subwatershed #340 average percent change in average annual surface runoff
for all scenarios 13
Figure 9: Subwatershed #340 average percent change in average annual sediment
yield for all scenarios 14
Figure 10: Spatial distribution of anthropogenic land use in the year 2100 under
scenarios Bl and A2 15
Figure 11: Effect of reservoirs modeled in SWAT on sediment yield at the watershed
outlet and below Strontia Springs Reservoir 16
Figure 12: Effect of reservoirs modeled in SWAT on streamflow below Strontia
Springs Reservoir and at the watershed outlet 17
Figure 13: Sediment yield within the South Platte River Basin, emphasis on the
Strontia Springs Reservoir (reservoir #151) where sediment yield decreases
downstream of the impoundment. Bar graph illustrates the difference in
sediment yield portrayed in the map as a percentage of sediment yield
flowing above Strontia Springs 18
Figure 14: Change in Human Use Index (HUI), average annual sediment yield, and
average annual surface runoff in percent from 2010 to 2100 for scenario Al 19
Figure 15: Change in Human Use Index (HUI), average annual sediment yield, and
average annual surface runoff in percent from 2010 to 2100 for scenario A2 20
Figure 16: Change in Human Use Index (HUI), average annual sediment yield, and
average annual surface runoff in percent from 2010 to 2100 for scenario Bl 21
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Figure 17: Change in Human Use Index (HUI), average annual sediment yield, and
average annual surface runoff in percent from 2010 to 2100 for scenario B2 22
Figure 18: Change in Human Use Index (HUI), average annual sediment yield, and
average annual surface runoff in percent from 2010 to 2100 for scenario BC 23
Figure 19: ArcMap Geoprocessing Model that Clipped, Projected, and Reclassified the
ICLUS Data into Classified Land Cover for use in AGWA 29
Figure 20: Example of inputs used to run AGWA-SWAT 30
Figure 21: Counties included when calculating estimated population growth by decade
under different scenarios within the South Platte River Basin 31
Figure 22: Results presented in this report represent a watershed average as well values
for specific streams and subwaterwsheds 32
Vlll
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List of Tables
Table 1: Summary of the types of changes of the different ICLUS scenarios (EPA 2009) 5
Table 2: Explanation of ICLUS housing density categories (EPA 2010) 6
Table 3: Reclassification Table for ICLUS Housing Density Classes to 2006 NLCD
Land Cover Types 8
Table 4: Reservoirs Included in the SWAT Modeling of the South Platte River Basin 9
Table 5: Change in Human Use Index over Time 33
Table 6: Change in Average Annual Surface Runoff over Time 33
Table 7: Change in Channel Average Annual Sediment Yield over Time 34
Table 8: Land Cover Change for Scenario Al from Baseline 2010 to 2100 (Note: Largest
Positive/Negative Changes are Highlighted Red/Orange; values in parentheses
are the percent change in cover type from the 2010 base case) 35
Table 9: Land Cover Change for Scenario A2 from Baseline 2010 to 2100 (Note: Largest
Positive/Negative Changes are Highlighted Red/Orange; values in parentheses
are the percent change in cover type from the 2010 base case} 36
Table 10: Land Cover Change for Scenario Bl from Baseline 2010 to 2100 (Note:
Largest Positive/Negative Changes are Highlighted Red/Orange; values in
parentheses are the percent change in cover type from the 2010 base case} 37
Table 11: Land Cover Change for Scenario B2 from Baseline 2010 to 2100 (Note:
Largest Positive/Negative Changes are Highlighted Red/Orange; values in
parentheses are the percent change in cover type from the 2010 base case} 38
Table 12: Land Cover Change for Baseline BC from Baseline 2010 to 2100 (Note:
Largest Positive/Negative Changes are Highlighted Red/Orange; values in
parentheses are the percent change in cover type from the 2010 base case} 39
IX
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X
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Acronyms and Abbreviations
ACOE U.S. Army Corps of Engineers
AGWA Automated Geospatial Watershed Assessment tool
ARS U.S.D.A Agricultural Research Service
BC Base Case
CWA Clean Water Act
CDSS Colorado Decision Support System
CFSR Climate Forecast System Re-analysis
DEM Digital Elevation Model
DST Decision Support Tool
EPA U.S. Environmental Protection Agency
FWS U. S. Fish and Wildlife Service
GIS Geographic Information System
HD Housing Density
HUI Human Use Index
ICLUS Integrated Climate and Land-Use Scenarios
IPCC Intergovernmental Panel on Climate Change
MRLC Multi-Resolution Land Characteristics Consortium
NED National Elevation Dataset
NCEP National Centers for Environmental Prediction
NLCD National Land Cover Database
NOAA National Oceanic and Atmospheric Administration
NRCS Natural Resources Conservation Service
ORD Office of Research and Development
RARE Regional Applied Research Effort
SRES Special Report on Emissions Scenarios
STATSGO State Soil Geographic Data Base
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SWAT Soil and Water Assessment Tool
USDA U.S. Department of Agriculture
USDI U.S. Department Interior
USGS U.S. Geological Survey
xn
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Abstract
Long-term land-use and land cover changes and their associated impacts pose critical
challenges to sustaining vital hydrological ecosystem services for future generations. In this
study, a methodology first developed on the San Pedro River Basin in southeastern Arizona was
used to characterize potential hydrologic impacts from future urban growth scenarios through
time on the South Platte River Basin. Future growth is represented by housing density maps
generated in decadal intervals from 2010 to 2100, produced by the U.S. Environmental
Protection Agency (EPA) Integrated Climate and Land-Use Scenarios (ICLUS) project. ICLUS
developed future housing density maps by adapting the Intergovernmental Panel on Climate
Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and
demographic storylines to the conterminous United States. To characterize hydrologic impacts
from future growth, the housing density maps were reclassified to National Land Cover Database
(NLCD) 2006 land cover classes and used to parameterize the Soil and Water Assessment Tool
(SWAT) using the Automated Geospatial Watershed Assessment tool (AGWA). The objectives
of this project were to 1) expand a methodology for adapting the ICLUS data for use in AGWA
as an approach to evaluate basin-wide impacts of development on streamflow runoff
characteristics and water quality in a large, complex watershed, 2) present qualitative results
from the application of the methodology to evaluate water scenario analyses related to a baseline
condition and projected changes, and 3) discuss implications of the analysis for water resources
and land management in the South Platte River Basin.
Introduction
Changes in land-use and land cover are critical in the determination of water availability,
quality, and demand. The consequences of human modification to the Earth's surface for
extraction of natural resources, agricultural production, and urbanization may rival those which
are anticipated via climate change (Vitousek 1994, Vorosmarty et al. 2000, Chapin et al. 2002,
DeFries and Eshleman 2004, Brauman et al. 2007, Whitehead et al. 2009, Triantakonstantis and
Mountrakis 2012). Responding to change requires improvements in our ability to identify
vulnerabilities and to develop processes and metrics to better understand the consequences of
choice. It also requires an ability to communicate highly technical information to risk managers
and decision makers.
Scenario analysis provides the ability to explore pathways of change that diverge from
baseline conditions and lead to plausible future states or events. It has been used extensively in
studies related to environmental decision support (USDI 2012). Although a number of scenario
frameworks are available to assist in evaluating policy or management options, most are
designed to analyze alternative futures related to decision options, potential impacts and benefits,
long-term risks, and policy and management paradigms (Steinitz et al. 2003, Kepner et al. 2012,
March et al. 2012). These frameworks are frequently combined with process modeling and are
intended to bridge the gap between science and decision making, and are effective across a range
of spatial and temporal scales (Liu et al. 2008a and 2008b, Mahmoud et al. 2009).
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This report draws substantial background from Burns et al. (2013) and uses the methodology
first developed on the San Pedro River Basin in southeastern Arizona. The methodology
integrates a widely used watershed modeling tool and national database with alternative future
scenarios which are scaled to regional and local applications. Expansion of the methodology for
the South Platte River Basin integrates some of the hydrologic complexities related to the
modeling of water in storage in the South Platte River and its tributaries to characterize potential
hydrologic impacts at different scales. This report describes the cumulative impact of housing
densities parsed out at decadal intervals to the year 2100 on a hydrological ecosystem that spans
from the Front Range of the Rocky Mountains to the North American Great Plains.
The U.S. Environmental Protection Agency (EPA) supports a watershed approach to
resource protection that emphasizes restoration and rehabilitation of ecosystem services to
enhance or maintain hydrologic function within the South Platte River Basin. In an effort to
improve the ability of environmental decision makers and managers to plan and respond to
potential change at the basin scale, the EPA, U.S. Department of Agriculture (USDA)
Agriculture Research Service (ARS), and the University of Arizona have recently initiated two
projects under the Regional Applied Research Effort (RARE) Program. The two case studies
selected for this project are the San Pedro River (U.S./Mexico) in EPA Region 9 and the South
Platte River Basin (CO, WY, and NE) in EPA Region 8.
For the purpose of this report, the results are focused on the South Platte River Basin. The
intent is to quantitatively evaluate hydrologic impacts of future developments at the basin scale,
which intrinsically addresses the cumulative impact of multiple housing development projects.
In order to understand the cumulative impact of housing development and land cover change, we
will look at relative changes in surface runoff, streamflow and sediment yield across the basin.
The study area encompasses the South Platte River Basin (-61,700 km2 or -23,820 mi2) from
the headwaters near Fairplay, CO to the outlet defined near Sutherland, NE (Figure 1). The
entirety of the South Platte River flows over 700 km (435 mi) from the headwaters in the Front
Range to its confluence with the North Platte River in North Platte, NE. A complex network of
water uses, land uses, and stakeholder priorities influence the quantity and quality of water that
flows in the South Platte River. The South Platte River Basin is home to over 70% of
Colorado's population which means water resources are allocated to their growing municipalities
as well as agricultural and industrial sectors (Dennehy et al. 1993). Vegetation transitions from
evergreen and deciduous forest in the Upper South Platte to range and grasslands in the Lower
South Platte. High elevation, forested subwatersheds contribute a substantial amount of water
resources to the river basin via snowmelt and summer precipitation (Dennehy et al. 1993).
Since the 19th century, the South Platte River has been developed in order to provide
beneficial uses which include municipal water supply, agricultural water supply, water for
recreation, and water for aquatic life (Eschner et al. 1983; Saunders and Lewis 2003). Spring
snowmelt and heavy precipitation is stored, utilized, and most returned to the mainstem river
after use via a number of canals, dams, and reservoirs throughout the watershed (Kircher and
Karlinger 1983). Natural features such as riparian habitat, wetlands and marshes along the main
channel and its tributaries provide essential ecosystem services in the form of erosion control,
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flood control, nutrient filtering, avian habitat, and aquatic habitat (Rapport et al. 1998; Strange et
al. 1999; Naiman and Decamps 1997; Novitzki et al. 1997). Conservation, restoration and
rehabilitation of these wetlands is a priority of the EPA and U.S. Army Corps of Engineers
(ACOE); additionally constructed wetlands have been used to augment ecosystem services in
recently developed areas for the purpose of flood control and water quality improvements.
Temperature Stations
Precipitation Stations
Sources: Esri, DeLorme. HERE, TomTom. Intermap, increment P Corp . GEBCO USGS FAO, NPS
NRCAN, GeoBase. IGN Kadaster NL, Ordnance Survey, Esn Japan METI, Esn China (Hong Kong)
swisstopo, and the CIS User Community
Figure 1: Location map of the study area with CFSR points for precipitation and weather generating stations for
the South Platte River Basin.
An underlying premise of this project is that watershed assessments can be significantly
improved if environmental resource managers have Decision Support Tools (DSTs) that are
easy-to-use, access readily available data, and are designed to address hydrologic and water
quality processes that are influenced by development at both the project- and basin-wide scale.
The Automated Geospatial Watershed Assessment tool (AGWA; Miller et al. 2007;
http://www.epa.gov/esd/land-sci/agwa/index.htm andhttp://www.tucson.ars.ag.gov/agwa), the
DST used in this project, will assist the EPA and other agencies with permitting and enforcement
responsibilities under Clean Water Act (CWA) Sections 401, 404 (FWS, NOAA, and ACOE),
402, 311 (US Coast Guard and states), and CWA 319 grant recipients (states, tribes, and local
organizations). AGWA is recognized as one of the world's primary watershed modeling systems
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(Daniel et al. 2011) providing the utility to generate hydrologic responses at the subwatershed
scale and spatially visualize results for qualitative comparisons (also see
MIIK/MEU^ Qualitative or
relative comparisons are presented without the calibration of baseline data. Results presented in
the form of relative change can be interpreted by decision makers to identify areas that are most
sensitive to environmental degradation as well as areas of potential mitigation or enhancement
opportunities, and thus inform restoration, permitting, and enforcement strategies.
Methods
The methodology developed to ascertain local vulnerabilities and qualitative cumulative
impacts associated with basin-wide development is a multi-step process. Project/watershed
extent must first be defined in order to obtain data that covers the entire study area. Various land
cover data must be converted to the appropriate format, compatible with AGWA, and soil inputs
must be acquired. Daily precipitation records need to be located and formatted for the
watershed, and daily weather generator stations for temperature, humidity, wind, and solar
radiation data must be defined. Reservoirs need to be located, selected and setup for the Soil and
Water Assessment Tool (SWAT; Neitsch et al. 2002; Srinivasan and Arnold 1994). Finally,
AGWA is used to parameterize SWAT, the reservoirs are added to SWAT, and SWAT is
executed for the baseline condition (2010) and future land cover/use scenarios (2020-2100).
Project/Watershed Extent
The first step of the methodology is defining an accurate project and watershed extent. The
extent is used to locate other required data including land cover, soils, precipitation, climate and
reservoir features. To define the project extent, the watershed is delineated in AGWA and given
a buffer distance of five kilometers to ensure there are no gaps in coverage for the land cover and
soils data. The watershed was delineated using a 30-meter digital elevation model (DEM) that
had been hydrologically corrected to ensure proper surface water drainage (Appendix B - Figure
20). Modification of the DEM in this region, which involved "burning" in streams (Saunders
1999), was necessary to enforce proper drainage at the reservoirs due to the complicated network
of diversions and canals that flow into and out of reservoirs. In the United States, the U.S.
Geological Survey (USGS) The National Map Viewer and Download Platform
maintains the National Elevation Dataset (NED;
, which is one recommended source for DEM data.
Land Cover
The land cover data used in this report comes from two sources. The National Land Cover
Database 2006 (NLCD; Fry et al. 201 1), available nationally, is used as the base land cover for
the South Platte River Basin. The NLCD 2006 was used for the base land cover because it was
the most current dataset for the United States at the time of modeling and because NLCD (from
2001 instead of 2006) has been used previously with ICLUS data to project future growth
(Johnson et al. 2012). At the time of writing, the Multi-Resolution Land Characteristics
Consortium (MRLC) has released a newer NLCD product, NLCD 201 1 (Jin 2013). The ICLUS
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project data were identified as an ideal dataset for projecting basin-wide development into the
future because its national-scale housing-density (HD) scenarios are consistent with the
Intergovernmental Panel on Climate Change (IPCC 2001) Special Report on Emissions
Scenarios (SRES; Nakicenovic and Swart 2000) greenhouse gas emission storylines (Bierwagen
et al. 2010; EPA 2009; EPA 2010; Table 1, Figure 2).
Table 1: Summary of the types of changes of the different ICLUS scenarios (EPA 2009).
National Scenario
Al
Bl
A2
B2
Base
Case
(2000)
medium population growth; fast
economic development; high
global integration
medium population growth; low
domestic migration resulting in
compact urban development
high population growth; greatest
land conversion; high domestic
migration resulting in new
population centers
moderate economic
development; medium
population growth; medium
international migration
U.S. Census medium scenario
Demographic Model
Fertility
low
low
high
medium
medium
Domestic
Migration
high
low
high
low
medium
Net
International
Migration
high
high
low
low
medium
Spatial Allocation Model
Household
Size
smaller
(-15%)
smaller
(-15%)
larger
(+15%)
no change
no change
Urban
Form
no change
slight
compaction
no change
slight
compaction
no change
The ICLUS HD data was combined with the NLCD data to project future development by
decade to 2100. The ICLUS data has five categories of housing density representing rural,
exurban, suburban, urban, and commercial/industrial (Table 2).
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Table 2: Explanation of ICLU S housing density categories (EPA 2010).
Class
99
4
3
2
1
Acres Per
Housing Unit
NA
O.25
0.25-2
2-40
>40
Housing Units
Per Acre
NA
>4
0.5-4
0.025-0.5
O.025
Hectares Per
Housing Unit
NA
<0.1
0.1-0.81
0.81-16.19
>16.19
Housing Units
Per Hectare
NA
>10
1.23-10
0.06-1.23
O.06
Density Category
Commercial/Industrial
Urban
Suburban
Exurban
Rural
The ICLUS database produced five seamless, national-scale change scenarios for urban and
residential development (Table l). The scenarios were developed using a demographic model to
estimate future populations through the year 2100 and then allocated to 1-hectare pixels by
county for the conterminous United States (EPA 2009; EPA 2010). The final datasets provide
decadal projections of both housing density and impervious surface cover from the 2010 baseline
year projected out to the year 2100. The A2 Scenario is characterized by high fertility, high
domestic migration and low net international migration; it represents the highest population gain
of 690 million people in the United States by 2100 (Figure 2). The Base Case (BC) and the B2
Scenario are the middle scenarios, with medium fertility and medium to low domestic and
international migration. An intermediate output of the ICLUS project provides population data
by county for the conterminous United States projected by decade from the baseline (2010) to
2100; these population values were used to drive housing density growth (ORD 2007; EPA
2009). This dataset was also used to estimate population growth for the entire conterminous
United States (Figure 2). For the South Platte River Basin, counties that intersected the basin were
extracted and their projected populations were summed by decade in order to get an estimate of
population growth for each scenario (Figure 3 and Appendix C - Figure 21).
Aside from population growth, differences in the way housing is allocated reflect a division
in scenarios; sprawling development is inherent in scenarios Al, A2, and BC while compact
growth patterns are reflected in scenarios Bl and B2. As a result of this distinction, the county
populations in urban and suburban areas generally grow faster than in rural areas in the BC, but
the experiences of individual counties vary. Al and Bl, with low fertility and high international
migration, are the lowest of the population scenarios. The primary difference between these
scenarios occurs at the domestic migration level, with an assumption of high domestic migration
under Al and low domestic migration under B1. The effect of different migration assumptions
becomes evident in the spatial model when the population is allocated into housing units across
the landscape. The A2 scenario results in the largest changes in urban and suburban housing
density classes and greater conversion of natural land-cover classes into new population centers,
or urban sprawl. The largest shift from suburban densities to urban occurs in 2050-2100 for the
A-family scenarios (Bierwagen et al. 2010).
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Population Projections for Conterminous US (2010-2100)
300
650
c
o
-| 500
Q.
O
Q.
350
200
— t— Baseline
A2
- B2
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Figure 2: Population projections for ICLUS scenarios by decade (EPA 2009).
Population Projections for South Platte River Basin*
(2010-2100)
c
o
Q.
O
Q.
—I—Baseline
-*-Al
-•-A2
-A-B1
-W-B2
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Figure 3: Population projections for ICLUS scenarios by decade for counties that intersect the South Platte
River Basin (ORD 2007; Appendix B - Figure 21).
The NLCD data has different land cover classes, a different projection, and is at a different
resolution (30 meters) than the ICLUS data (100 meters); therefore, the ICLUS data were pre-
processed for use in this project. Preprocessing includes clipping the ICLUS data to the extent of
the buffered South Platte River Basin, projecting the ICLUS data to UTM Zone 13 NAD 83,
reclassifying the ICLUS data to NLCD classes (Table 3), and resampling the ICLUS data from
100 m to 30 m using the nearest neighbor assignment. The resulting dataset was then merged
with the NLCD dataset so that ICLUS data replaced the NLCD data if there was a change in
ICLUS housing density. The reclassification scheme was determined based on housing density
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definitions, which were different between the two datasets. As a result the "rural" land cover
type in the ICLUS data was defaulted to the existing NLCD class over the extent. This
methodology was incorporated into a tool in ArcToolbox in ESRI ArcMap for easy conversion
of the ICLUS datasets (Appendix A, Figure 19).
Table 3: Reclassification Table for ICLUS Housing Density Classes to 2006 NLCD Land Cover Types.
ICLUS Data
Code
1
2
3
4
99
Land Cover Type
Rural
Exurban
Suburban
Urban
Commercial/Industrial
2006 NLCD
Code
-
22
23
24
24
Land Cover Type
Default to NLCD cover type
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Developed, High Intensity
Ten land cover datasets per scenario (50 total) were produced from the combination of the
NLCD datasets and the ICLUS datasets, representing the change in landscape attributed to
population and development changes per decade from 2010 to 2100 (Guy et al. 2011;
EPA/600/C-12/0001). Table 8 through Table 12 in Appendix D contains the changes in land
cover/use by decade for each of the ICLUS national scenarios. For each scenario, the converted
ICLUS dataset from 2010 was used as the project baseline to which the successive decadal
datasets were compared.
Soils
Soils data for the South Platte River Basin were obtained from the Natural Resources
Conservation Service (NRCS) - Web Soil Survey. The Digital General Soil Map of the United
States, or STATSGO, was downloaded for Wyoming, Nebraska and Colorado and subsequently
merged to create a continuous soil layer that covered the entire project extent. The mapping
scale of STATSGO is 1:250,000 (USDA-NRCS 1994).
Precipitation
Precipitation data was obtained from "Global Weather Data for SWAT"
Globalweather 2014). This site utilizes the National Centers for
Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data for a 32-
year period from January 1, 1979 through December 31, 2010 (Saha et al. 2010; Fuka et al.
2013). This source was used since it is easily available and downloadable in the required SWAT
format. CFSR data includes daily precipitation amounts and was downloaded from stations
within the project extent. CFSR interpolated point locations are given in geographic coordinates
and are inserted into a feature class within ArcMap for use in AGWA. Precipitation data from
1979-2010 was reformatted for implementation in AGWA and missing dates were populated
with "-99"; SWAT uses a built-in stochastic weather generator to determine how much
precipitation to supply for the missing records. A total of 63 weather stations were used to
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generate precipitation and seven weather generating stations (WON) were used to determine
temperature inputs across the South Platte River Basin (Figure 1; Appendix B - Figure 20).
Precipitation and temperature remain constant throughout the simulations.
Reservoirs
Reservoir information is maintained by multiple local, state, and federal entities. A large
portion of this information can be found at the Colorado Decision Support Systems (CDSS)
webpage under the heading of "Structures (Diversions)" at
kflj)://MMJ^^ (CDSS 2014). The CDSS
maintains records about structures as well as Geographic Information System (GIS) data by
region. Within the South Platte River Basin in Colorado, the CDSS GIS layer documents 3,451
reservoirs of all shapes and sizes. The CDSS GIS product provides a web link to documented
structure records for each reservoir; the records include monthly reservoir releases which are
required for SWAT modeling. The United States Geological Survey also maintains a dataset of
"Large" reservoirs for the entire United States. This dataset describes reservoirs that were
completed prior to the start of 1988 with a normal capacity of at least 5,000 acre-feet or a
maximum capacity of at least 25,000 acre-feet (Ruddy and Hitt 1990); normal capacity is defined
by Ruddy and Hitt as the "total storage space, in acre-feet, below the normal retention level"
(1990). The GIS layer produced from this dataset contains surface area and volume
measurements for most large reservoirs across the nation.
Both the CDSS and USGS records were used to incorporate reservoir processes into the
AGWA-SWAT modeling. Both layers were clipped to the extent of the South Platte River
Basin. The clipped USGS reservoir locations were visually confirmed in ArcMap using a
basemap and then clipped to a 500 meter buffer of the basins stream features in order to remove
off-stream reservoirs from the analysis. Ten USGS records remained and the reservoir
identifications were then compared with the 3,451 CDSS features to check for release records.
In this case, four reservoirs were dropped from the analysis because they did not have the
required monthly release information or were only cataloged in either the CDSS dataset or the
USGS dataset. Without the necessary inputs for SWAT we were unable to include these four
reservoirs in the modeling. The final point layer contained six reservoirs which were to be
modeled in SWAT (Figure 1; Table 4).
Table 4: Reservoirs Included in the SWAT Modeling of the South Platte River Basin.
Reservoirs
Number
150
35
19
151
20
85
Name
Spinney Mountain
Eleven Mile
Cheesman
Strontia Springs
Cherry Creek
Milton Seaman
Normal Capacity (104 m3)
6722
12063
9752
950
1722
618
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SWAT requires surface area and volume of the reservoir at the principle spillway and at the
emergency spillway; measurements were found in the USGS data for volume in acre-feet, which
was converted to cubic meters, and for surface area, which was given in acres and converted to
hectare. For example, normal capacity given in the USGS dataset was used as an input for
volume at the principle spillway. SWAT also utilizes minimum and maximum average daily
release information summarized by month as well as average daily release values, by month, in
cubic meters per second which were given and/or calculated from the CDSS records. ArcMap
was used to relate reservoirs with stream reach and subwatershed. Those relationships were used
to manually alter the SWAT input files that were written by AGWA. SWAT was then executed
outside of AGWA and the results were imported into the GIS environment for visualization.
AGWA-SWAT Modeling
The AGWA tool was used to model the South Platte River Basin with SWAT. The AGWA
tool is a user interface and framework that couples two watershed-scale hydrologic models, the
KINematic Runoff and EROSion model (KINEROS2; Semmens et al. 2008; Goodrich et al.
2012) and SWAT (Arnold et al. 1994; Tuppad et al. 2011), within a GIS. In addition to the
coupling of hydrologic models and GIS, the AGWA tool performs watershed delineation and
characterization, model parameterization, execution, and watershed assessment at multiple
temporal and spatial scales, and visualization of model simulation results (Daniel et al. 2011).
Current outputs generated through use of the AGWA tool are runoff (volumes and peaks) and
sediment yield, plus nitrogen and phosphorus with the SWAT model.
SWAT can also model the movement of water and sediment through a reservoir based on
user defined inputs which will alter volume of water and sediment flowing through modeled
channel networks. SWAT uses surface area, volume and average daily outflow summarized by
month to calculate volume of outflow and changes in reservoir volume over the simulation
period. For controlled reservoirs, the model is based on static outflow rates that represent
historic averages measured at each of the six reservoirs included in the simulation; the model
also uses weather inputs based on the subwatershed that the reservoir resides in; temperature and
precipitation information is also constant throughout the simulations. SWAT was used to model
the relative impact of water and sediment trapping in large reservoirs within the South Platte
River Basin. However, in order to comprehensively assess water quality and quantity impacts at
lakes and reservoirs, other hydrologic models with reservoir water quality components would be
required (Narasimhan et al. 2009).
The South Platte River Basin was subdivided into 61 subwatersheds and 42 channels.
Subdivided watersheds were characterized using a 30 meter DEM and derived flow direction and
flow accumulation grids, STATSGO soils, 66 precipitation stations, seven WGN stations, and
the 50 land cover datasets (produced by combining ICLUS and NLCD datasets at decadal
intervals) to produce 50 different simulations. SWAT was executed outside of AGWA so the six
large reservoirs within the basin could be incorporated into the model, but model results were
visualized within AGWA. AGWA facilitates the identification of areas more
susceptible/sensitive to environmental degradation and also areas for potential mitigation or
enhancement by mapping spatially distributed modeling results back onto the watershed.
10
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Results
All scenarios resulted in an increase to the Human Use Index (HUI) metric averaged over the
entire watershed. HUI (adapted from Ebert and Wade, 2004) is the percent of land area in use by
humans. It includes NLCD land cover classes "Developed, Open Space"; "Developed, Low
Intensity"; "Developed, Medium Intensity"; "Developed, High Intensity"; "Pasture/Hay"; and
"Cultivated Crops". HUI was calculated for each scenario and decade with absolute differences,
in percent, representing the change in HUI from the base year 2010. The ICLUS A2 scenario
resulted in the largest increase of the HUI, 1.8% in year 2100 for the entire watershed (Figure 4
and Appendix C - Table 5). Land cover conversion to developed classes from low to high
intensity slowed in the second half of the century across the whole watershed; under scenario A2
peak growth of "Developed, Low Intensity" happened in 2050 at 12.8%, followed by a peak of
"Developed, Medium Intensity" in 2060 at 69.0%, and finally "Developed, High Intensity" in
2100 at 160.2% (Appendix D - Table 9).
Similar to the increases in HUI over the entire watershed, both simulated runoff and sediment
yield increased at the watershed outlet over time for all scenarios; scenario A2 experienced the
largest percent change in surface runoff and sediment yield, 2.7% (see Figure 5, Figure 6, and
Appendix C - Table 6 and Table 7). Percent change, for every metric excluding HUI, was
calculated using the following equation:
where [decadet] represents simulation results for a decade from 2020 through 2100 for a given
scenario (/') and [baset] represents the baseline 2010 decade for the same scenario.
HUI Change 2010-2100 (Entire Watershed)
38.9%
—*—Scenario Al
—•—Scenario A2
—^-Scenario Bl
—*— Scenarios 2
—*— Baseline BC
36.4%
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Figure 4: Watershed Human Use Index (HUI) for all scenarios.
11
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Change in Surface Runoff 2010-2100 (Entire Watershed)
-4— Scenario Al
-•-Scenario A2
-±— Scenario Bl
Scenario B2
—I—Baseline BC
0%
2010 2020 2050 2040 2050 2060 2070 2080 2090 2100
Figure 5: Watershed average percent change in average annual surface runoff for all scenarios.
Change Sediment Yield 2010-2100 (Watershed Outlet)
-*—Scenario Al
••—Scenario A2
Scenario Bl
-^Scenario 02
-^Baseline BC
2010
2023
2030
2040
2050
2060
2070
2080
2090
2100
Figure 6: Watershed average percent change in average annual sediment yield for all scenarios.
In contrast to the relatively low percent change at the whole watershed scale, notably larger
changes were seen in some sub watersheds. In scenario A2, the scenario with the most
population growth, one subwatershed (#340; Appendix C - Figure 22), resulted in a much larger
increase of up to 9.5 % in the HUI in year 2100 (subwatershed #340; Figure 7 and Appendix C-
Table 5). This subwatershed saw increases in surface runoff and sediment yield of 17.6% and
15.1% respectively, corresponding to the increased HUI (Figure 8, Figure 9, and Appendix C -
Table 6 and Table 7). This contrast is indicative of the spatial variability of growth within the
South Platte River Basin, where projected growth is concentrated in the higher elevations near
Denver, CO.
12
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HUI Change 2010-2100 (Subwatershed #340)
62"/,
—*—Scenario Al
-•-Scenario A2
-it-Scenario Bl
— Scenario B2
—I— Baseline BC
49%
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Figure 7: Subwatershed #340 Human Use Index (HUI) for all scenarios.
Change in Surface Runoff 2010-2100 (Subwatershed #340)
—*—Scenario Al
-•-Scenario A2
Scenario Bl
-•*— Scenario B2
—I—Baseline BC
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Figure 8: Subwatershed #340 average percent change in average annual surface runoff for all scenarios.
13
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Change in Sediment Yield 2010-2100 (Subwatershed #340 Outlet)
;, Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
2010 2020 2030 2040 2050
2060 2070 2080 2090 2100
Figure 9: Subwatershed #340 average percent change in average annual sediment yield for all scenarios.
The spatial distribution of land cover change in 2100 under scenario Bl and A2 can be seen
across the watershed in Figure 10, where anthropogenic land use includes areas that were used to
calculate the HUI and "undeveloped" areas are those that are left unaffected by humans through
2100. In scenario A2, 63.3% of the watershed is unaffected by humans in 2010, this area
decreases to 61.6% in 2100 because of housing development. Figure 10 divides HUI or
anthropogenic land use into two categories that exhibit distinct hydrologic function.
"Anthropogenic Land Use - Developed" represents NLCD classes "Developed, Open Space";
"Developed, Low Intensity"; "Developed, Medium Intensity"; "Developed, High Intensity".
Whereas "Anthropogenic Land Use - Agriculture" includes NLCD classes "Pasture/Hay"; and
"Cultivated Crops". Noticeably less land cover change is projected to occur between 2010 and
2100 in the Great Plains region where "Anthropogenic Land Use - Agriculture" is dominant;
HUI change is minimal in those areas and can be seen in Figure 14 through Figure 18 along with
associated changes in water and sediment yield.
14
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Anthropogenic Land Use in the South Platte River Basin
Scenario B1
HUI2ioo = 37.3%
Change in HUI2ioo-2oio = 0.6%
Land Use 2100
| | Undeveloped i\
] Anthropogenic Land Use - Developed
I Anthropogenic Land Use -Agriculture
Scenario A2
HUI2ioo = 38.4%
Change in HUI2ioo-2oio = 1.8%
0 15 30 60 90 120
0 12.5 25 50 75 100
• • ^^^^ =^^^^ Miles
Figure 10: Spatial distribution of anthropogenic land use in the year 2100 under scenarios B1 and A2.
15
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In order to understand the effect of reservoirs on water and sediment yield, scenario A2 year
2010 was executed with and without the six reservoirs. The results of SWAT reservoir modeling
indicated an impact on the amount of sediment and water yield through streams in the South
Platte River Basin (Figure 11 and Figure 12). Reservoirs appear to have a more pronounced
impact on the stream reach and subwatershed scale where reductions in sediment and streamflow
were 93.3% and 78.7%, respectively; impacts of reservoirs were still seen at the watershed outlet
where reduction in sediment yield was 52.9% and streamflow was reduced by 39.0% (Figure 11
and Figure 12). Changes in land cover from 2010 to 2100 are projected to impact reservoir
sedimentation and thus storage which can be seen in Figure 13. The bar graph in Figure 13
shows that in 2010 under scenario A2, SWAT predicts only 15.8% of the sediment yield above
Strontia Springs Reservoir will remain at the outlet of subwatershed #340, downstream of the
reservoir. In 2100, SWAT modeling predicted 18.0% of sediment entering the reservoir will
remain in the channel at the outlet of subwatershed #340. This indicates storage of water and
sedimentation in the reservoir. Since historic reservoir water quality was not used for these
simulations, sediment yield above and below Strontia Springs Reservoir is presented as a
percentage of sediment flowing into the reservoir; absolute values reported are to emphasize
reservoir function. The six reservoirs modeled in SWAT for this report had an impact on
sediment yield and water yield within the South Platte River Basin.
Effect of Reservoir on Sediment Yield
300,000
250,000
o 200,000
- Without Reservoir
_ With Reservoir
Below Strontia Springs At Watershed Outlet
Figure 11: Effect of reservoirs modeled in SWAT on sediment yield at the
watershed outlet and below Strontia Springs Reservoir.
16
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Effect of Reservoir on Streamflow
ISO
.Without
Reservoir
. With Reservoir
Below Strontia Springs At Watershed Outlet
Figure 12: Effect of reservoirs modeled in SWAT on streamflow below
Strontia Springs Reservoir and at the watershed outlet.
Figure 14 through Figure 18 depict the percent change of FUJI, channel sediment yield, and
sub watershed surface runoff from 2010 to 2100 for each of the 5 ICLUS scenarios. The changes
in HUI relate well to the changes in sediment yield and surface runoff. The figures show the
impact of growth within the subwatersheds and contrast that variability with averaging the
impacts over the entire watershed as presented in Table 6 and Table 7. Table 8 through Table 12
in Appendix D present absolute and relative changes in land cover/use across the entire South
Platte River Basin.
17
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South Platte Watershed
Sediment Yield
Scenario A2
Year 2010
Year 2100
Above
Strontia
Springs
Below
Strontia
Springs
Streams
Sediment Yield (tons)
0.0-10400.0
10400.1 -27660.0
27660.1 -53420.0
53420.1 -92820.0
92820.1 - 125000.0
Subwatersheds
Sediment Yield (tons/hectare)
0.0-0.3
0.4-1.1
1.2-2.3
2.4-6.1
6.2-15.1
Figure 13: Sediment yield within the South Platte River Basin, emphasis on the Strontia Springs Reservoir
(reservoir #151) where sediment yield decreases downstream of the impoundment. Bar graph
illustrates the difference in sediment yield portrayed in the map as a percentage of sediment yield
flowing above Strontia Springs.
18
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0 1530 60 90 120
0 15 3 90 120
South Platte Watershed
Change Between 2010 - 2100
I A
A
Baseline A1
Subwatersheds
Human Use Index (Change)
I I 0 - 0.99
I |1 -3.12
^•3.13-5.6
^•5.61 -9.43
^•9.44-18.08
• Reservoirs
Streams
Sediment Yield (% Change)
-0.1 -0.0
0.1 -0.7
0.8-1.5
^—1.6-5.0
Subwatersheds
Runoff (% Change)
| | -3.9-0.0
| | 0.1 -0.8
H 0.8-3.3
H 3.4- 10.9
^•j 10.9-26.1
Figure 14: Change in Human Use Index (HUI), average annual sediment yield, and average annual surface
runoff in percent from 2010 to 2100 for scenario Al.
19
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0 1530 60 90 120
0 15 30 90 120
South Platte Watershed
MHOS Change Between 2010 - 2100
11
A
Baseline A2
Subwatersheds
Human Use Index (Change)
1 HI 0-0.99
^•3.13-5.6
^•5.61 -9.43
••9.44-18.08
• Reservoirs
Streams
Sediment Yield (% Change)
-0.1 -0.0
0.1 -0.7
0.8-1.5
^—1.6-5.0
^—5.1 -15.3
Subwatersheds
Runoff (% Change)
| | -3.9 - 0.0
| | 0.1 -0.8
• 0.8-3.3
•• 3.4-10.9
^H 10.9-26.1
Figure 15: Change in Human Use Index (HUI), average annual sediment yield, and average annual surface
runoff in percent from 2010 to 2100 for scenario A2.
20
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0 1530 60 90 120
0 15 30 ^~* 90 120
South Platte Watershed
Miles Change Between 2010 - 2100
i N
A
Baseline B1
Subwatersheds
Human Use Index (Change)
I I 0 - 0.99
ZM -3.12
^•3.13-5.6
^•5.61 -9.43
^•9.44-18.08
• Reservoirs
Streams
Sediment Yield (% Change)
-0.1 -0.0
0.1 -0.7
0.8-1.5
^—1.6-5.0
^—5.1 -15.3
Subwatersheds
Runoff (% Change)
I |-3.9-0.0
I | 0.1 -0.8
IBI 0.8-3.3
^H 3.4- 10.9
^B 10.9-26.1
Figure 16: Change in Human Use Index (HUI), average annual sediment yield, and average annual surface
runoff in percent from 2010 to 2100 for scenario Bl.
21
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0 1530 60 90 120
0 15 30 ^~* 90 120
South Platte Watershed
Miles Change Between 2010 - 2100
i N
A
Baseline B2
Subwatersheds
Human Use Index (Change)
I 10-0.99
Z|1 -3.12
••3.13-5.6
•• 5.61 - 9.43
••9.44-18.08
• Reservoirs
Streams
Sediment Yield (% Change)
-0.1 -0.0
0.1 -0.7
0.8- 1.5
^—1.6-5.0
^—5.1 -15.3
Subwatersheds
Runoff (%Change)
I |-3.9-0.0
| | 0.1 -0.8
^B 0.8-3.3
^H 3.4-10.9
^H 10.9-26.1
Figure 17: Change in Human Use Index (HUI), average annual sediment yield, and average annual surface
runoff in percent from 2010 to 2100 for scenario B2.
22
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0 1530 60 90 120
0 15 30 ^~* 90 120
South Platte Watershed
Miles Change Between 2010 - 2100
i N
A
Baseline BC
Subwatersheds
Human Use Index (Change)
I | 1.1 -3.1
•I 3.2-5.6
•• 5.7 - 9.4
••9.5-18.1
• Reservoirs
Streams
Sediment Yield (% Change)
-0.1 -0.0
0.1 -0.7
0.8-1.5
^—1.6-5.0
^—5.1 -15.3
Subwatersheds
Runoff (%Change)
I |-3.9-0.0
I | 0.1 -0.8
^B 0.8 -3.3
^H 3.4-10.9
^H 10.9-26.1
Figure 18: Change in Human Use Index (HUI), average annual sediment yield, and average annual surface
runoff in percent from 2010 to 2100 for scenario BC.
23
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Discussion
The results produced by the AGWA-SWAT modeling represent a qualitative assessment of
anticipated hydrologic change resulting from the ICLUS Al, A2, Bl, B2, and BC scenarios.
CFSR rainfall and climate data are used to drive the SWAT model and held constant throughout
the simulations so that conclusions can be drawn related to the impacts of predicted land cover
change under the different ICLUS scenarios. Using static rainfall and climate data, the results of
those simulations do not account for anticipated climate change, although climate change may
amplify or abate the results presented here based on predicted changes to temperature and
precipitation characteristics. Quantitative assessments of anticipated hydrologic impacts resulting
from the ICLUS scenarios would require rainfall and climate observations, calibration of the
baseline (2010) for each scenario, and additional information to parameterize future decades,
including but not limited to the design and placement of flood mitigation measures (detention
basins, riparian buffers, water harvesting, recharge wells, open space infiltration galleries,
constructed wetlands etc.) that would be a required component of any future development.
All the ICLUS scenarios show limited impact to the landscape at the watershed scale which
is also reflected by limited hydrologic impacts at the same scale. Impacts are more pronounced
at the subwatershed level where the effects of land cover change are not averaged out by the
large metropolitan area supported by large cropland and pasture developments contained in the
-61,700 km2 watershed. Under all five scenarios, in the baseline year 2010, at least one-third of
the land area in the South Platte River Basin was classified under anthropogenic land use, which
includes land developed for housing as well as land used for agricultural production. Under the
most dramatic development scenario, A2, the HUI or percentage of anthropogenic land use only
increased by 1.8% in 2100.
The methodology presented herein uses HUI as a quantifiable metric for land cover change
resulting from urban growth; however, it does not distinguish between different types of human
use. Different types of human use, ranging from "Developed, Open Space" to "Developed, High
Intensity" to "Cultivated Crops" have different hydrologic properties associated with them.
Despite the observed relationship between increasing HUI and increasing surface runoff and
sediment yield in the results, HUI cannot be used as a surrogate for hydrologic modeling, which
more closely captures the actual land cover properties and the complex interactions and
feedbacks that occur across a watershed.
The greatest changes in surface runoff occur in subwatersheds where the change in HUI was
also greatest; accordingly, the smallest changes in surface runoff occur in areas where the change
in HUI was smallest. Sediment yield in the channels is largely driven by surface runoff, so
channels immediately downstream of subwatersheds with high changes in HUI and surface
runoff experience the largest changes in sediment yield. It is apparent that changes driven by
anthropogenic activities have an impact on hydrologic processes throughout the watershed.
24
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Land use can contribute to the rate of sediment deposition and contaminant delivery in
reservoirs and may impact their water-storage capacity and water quality, although this is not
accounted for in this analysis (Mau and Christensen 2000). Sedimentation in reservoirs reduced
the impact of sediment loads downstream and could mitigate water quality impairments in lower
stream reaches. While wetland function was not modeled in this analysis, it is important to note
that wetlands often control flooding, sediment yield and erosion. Land cover change predicted
by ICLUS resulted in losses of wetland across the entire watershed by the year 2100 ranging
from -2.9% under scenario Bl to -7.8% under scenario A2 (Appendix D - Table 8 through
Table 12). The impact of reservoirs, wetlands and diversions is apparent when looking at the
watershed average or outlet and will likely regulate future impacts downstream; however, this
effect is more pronounced at the subwatershed and stream reach scale.
The results emphasize the importance of investigating localized impacts to natural resources
at appropriate scales as the impacts at the subwatershed scale and below can be much more
significant than at the basin scale. Thus, any interests in cumulative effect of land use changes
should be addressed at the subwatershed scale versus the basin scale for this large western
watershed with significant public land holdings, or others like it which contain large tracts of
land that will likely remain undeveloped, and are therefore not subject to direct urbanization
impacts.
Also highlighted in the South Platte River Basin is the timing of development; the largest
increases in HUI under every scenario took place by 2050 after which only minor increases in
HUI occurred. Under scenario A2, by 2060 the entire watershed saw an increase in HUI of 1.6%
compared to the percent increase of HUI from 2010 to 2100 of 1.8%. This is likely due to a shift
which is observed among developed land cover classes; under every scenario there was a
transition from "Developed, Low Intensity" towards "Developed, High Intensity" by 2100
(Appendix D - Table 8 through Table 12). Corresponding to the overall increase in HUI are
large increases in sediment yield and surface runoff in the first five decades of analysis. Under
these circumstances, it is important to look at impacts under different temporal scales to
understand when management or mitigation measures will be most effective. The temporal and
spatial scales at which watersheds are evaluated are important factors to understanding how land
cover change will impact hydrologic ecosystem services.
Conclusions
The primary objective of this analysis was to evaluate the relative hydrologic impacts of
future growth through time, which was accomplished by using reclassified ICLUS housing
density data by decade from 2010 to 2100 to represent land cover in AGWA. AGWA is a GIS
tool initially developed to investigate the impacts of land cover change on hydrologic response at
different spatial and temporal scales to help identify vulnerable regions and evaluate the impacts
of management. Analyzing the hydrologic response of a watershed at multiple scales can
highlight vulnerable stream reaches or subwatersheds due to the extent of land use and land
cover change. AGWA also allows for assessment of basin-wide changes and cumulative effects
at the watershed outlet.
25
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ICLUS datasets were used for a number of reasons, including but not limited to their
availability (httg^^^ their use in a
similar EPA research effort (Johnson et al. 2012); the relative simplicity of their reclassification
to a product supported by AGWA; and the significant science behind the product (IPCC and
SRES consistent storylines). Reclassification was necessary to convert from housing density
classes to "developed" type classes in the 2006 National Land Cover Database. All land cover
classes of the NLCD are supported in AGWA via look-up tables which allow for translation of
land cover classes into hydrologic parameters necessary to parameterize the hydrologic models.
Changes in land cover/use under the A2 scenario resulted in the greatest predicted hydrologic
impacts due to a higher population growth rate and a larger natural land cover conversion rate.
The results of the analyses for all scenarios over the 2010-2100 year period (Table 6 and Table
7) indicate changes in the range of 1.0% (Bl scenario) to 2.7% (A2 scenario) in average annual
surface runoff across the watershed, and changes in the range of 0.7% (Bl scenario) to 2.7% (A2
scenario) in sediment yield at the watershed outlet. Investigating the results at the subwatershed
scale (smaller drainage areas for subwatershed #340), the changes in sediment yield are greater,
ranging from 4.9% (Bl scenario) to 15.1% (A2 scenario) and the change in surface runoff ranges
from 6.2% (Bl scenario) to 17.6% (A2 scenario).
Simulated increases in percent change of surface runoff and sediment yield closely tracked
increases in the HUI metric likely due to the development of impervious surfaces associated with
urbanization; consequently, growth and development should be moderated using green
infrastructure, low impact development, or other best management practices (BMPs) to prevent
large increases in surface runoff and sediment yield which could degrade water quality from
sediment and pollutant transport, erode and alter the stream channel, degrade or destroy habitat,
decrease biological diversity, increase sedimentation in reservoirs, and increase flooding. The
effects of growth may be magnified or mitigated by climate change or the implementation of
BMPs, though this is not accounted for in this analysis (Pyke et al. 2011). However, simplified
simulations that reflect cumulative potential impacts due to population growth and land
cover/use change may be used to inform water resource or land managers in the permitting,
planning and decision making context.
At present, issuance of ACOE CWA Section 404 individual permits are carried out in a
project-by-project fashion without much information to support an analysis of the collective
impacts of multiple projects on hydrology and biodiversity. However, the cumulative impact of
multiple projects on watershed function is a concern. From Part 11 (g) of Part 230 - Section
404(B) (1) Guidelines for Specification of Disposal Sites for Dredged or Fill Material
(Guidelines), "... cumulative impacts are the changes in an aquatic ecosystem that are attributable
to the collective effect of a number of individual discharges of dredged or fill material."
Although the impact of a particular discharge may constitute a minor change in itself, the
cumulative effect of numerous such changes can result in degradation and impairment of the
water resources, interfering with the productivity and overall integrity of biological, chemical,
and physical processes of aquatic ecosystems. Section 230.11 of the Guidelines describes
special conditions for evaluation of proposed permits to be issued, which includes the evaluation
26
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of potential individual and cumulative impacts of the category of activities to be regulated under
the general permit. The Guidelines constitute the substantive environmental criteria used in
evaluating activities regulated under Section 404. Section 404 requires a permit before dredged
or fill material may be discharged into the waters of the United States. The Guidelines state the
terms aquatic environment and aquatic ecosystem mean waters of the United States, including
wetlands, that serve as habitat for interrelated and interacting communities and populations of
plants and animals (part 230.3 [c]), and that "waters of the United States" includes tributaries
(part 230.3 [s]).
The quality and quantity of U.S. waters has been gaining public recognition in the face of
rapid development and climate change. Non-point source pollution contributing sediment or
pollutants from urban, agricultural, or natural lands can lead to the impairment of water
resources. Land cover/use change can often exacerbate water quality impairments and lead to
changes in local hydrology. In the Front Range, mountainous streams that are more sensitive to
water quality impacts could face increasing threat due to predicted land conversion and urban
development (Caulfield et al. 1987).
Identification of sensitive or vulnerable streams and subwatersheds within a large watershed
can be accomplished using AGWA. When used with current and accurate data, outputs from
AGWA can provide important information for land and resource managers utilizing the scenario
analysis approach. In order to best serve this purpose, it is important to use the most up to date
model inputs; future research will incorporate the updated NLCD 2011 data and potentially new
projections of land cover and land use change. A new framework in place of the SRES
storylines is the representative concentration pathways (RCPs) (van Vuuren et al. 2011); should
these be used to predict population and land cover change, the spatial data could also be used to
assess the impacts of different RCPs on watershed hydrology. In addition to updating land cover
inputs, future research could incorporate observed precipitation and temperature data along with
predicted changes in temperature and precipitation to understand the relative impact of climate
change in the South Platte River Basin. Utilizing a variety of scenarios can provide decision
makers with a suite of possible changes related to runoff and sediment yield from which to
develop new policy or management plans at different spatial and temporal scales.
Assessing possible impacts of land use, climate change, or management options across
multiple scales will highlight different vulnerabilities across a watershed. Local changes to
hydrology and sediment delivery at the subwatershed level are relevant because at those scales
the impacts tend to be much more significant. Additionally, in a large watershed such as the
South Platte, a single management option may not be viable from the forest to the plains.
Management, reclamation and restoration of hydrologic processes for the benefit of water
resources will be more effective at the subwatershed scale. Since hydrologic impacts are tied to
changes in land cover, these impacts at a watershed scale are expected to be limited. Large
watersheds will have complex hydrologic functions such as diversions, dams and reservoirs that
will impact the hydrologic characteristics at the watershed scale. In order to best inform land
and water resource managers about potential changes in watershed health and hydrology, a broad
range of scenarios needs to be assessed across different scales.
27
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Scenario analysis is an important framework to help understand and predict potential impacts
caused by decisions regarding conservation and development. For the EPA and other
stakeholders, hydrologic modeling systems (e.g. AGWA) integrated with internally-consistent
national scenario spatial data (i.e. ICLUS) provide an important set of tools that can help inform
land use planning and permitting, mitigation, restoration, and enforcement strategies.
28
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Appendix A
Figure 19: ArcMap Geoprocessing Model that Clipped, Projected, and Reclassified the ICLUS Data into Classified Land Cover for use in AGWA.
29
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Appendix B
Elevation
USGS 30 meter
DEM
Example of
AGWA-SWAT Inputs
Elevation
4347.5 m
887.1 m
Weather Generating Station
CFSR Precipitation Point
Reservoirs)
Soils
STATSGO
Land Cover
Scenario BC
Year 2010
Precipitation
and Weather
Reservoirs
Figure 20: Example of inputs used to ran AGWA-SWAT.
30
-------
State
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
NE
NE
NE
NE
NE
NE
NE
NE
WY
WY
County
Adams
Arapahoe
Boulder
Broomfield
Clear Creek
Denver
Douglas
El Paso
Elbert
Gilpin
Jefferson
Larimer
Lincoln
Logan
Morgan
Park
Sedgwick
Teller
Washington
Weld
Banner
Cheyenne
Deuel
Garden
Keith
Kimball
Lincoln
Perkins
Albany
Laramie
Counties Included in Population Estimate for the
South Platte River Basin
0 25 50
100 150 200
M^=^=]«JJJJJJJJJJJJJJJJJJJJJJJJI Kilometers
0 15 30
60 90
120
I Miles
Figure 21: Counties included when calculating estimated population growth by decade under different scenarios
within the South Platte River Basin.
31
-------
Appendix C
Features Selected for
Results
Sub-watershed #340
Outlet (Below Strontia
Springs Reservoir
Subwatershed
#340
Above Strontia
Springs Reservoir
Figure 22: Results presented in this report represent a watershed average as well values for specific streams
and subwaterwsheds.
32
-------
Table 5: Change in Human Use Index over Time.
HUI Base
2010
Change in Human Use Index from base
2020
2030
2040
2050
2060
2070
2080
2090
2100
Subwatershed #340 (Below Strontia Springs Reservoir)
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
51.2%
49.9%
50.2%
49.6%
50.2%
3.5%
3.8%
2.5%
2.4%
3.4%
5.8%
6.6%
3.6%
3.8%
5.8%
7.0%
8.2%
3.9%
4.4%
7.2%
7.3%
8.9%
4.0%
4.6%
7.9%
7.4%
9.2%
4.0%
4.7%
8.3%
7.5%
9.3%
4.0%
4.7%
8.3%
7.5%
9.4%
4.0%
4.7%
8.4%
7.5%
9.4%
4.0%
4.7%
8.4%
7.5%
9.5%
4.0%
4.7%
8.4%
Watershed Average
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
36.8%
36.7%
36.7%
36.6%
36.7%
0.6%
0.6%
0.4%
0.4%
0.6%
1.1%
1.1%
0.6%
0.6%
0.9%
1.3%
1.3%
0.6%
0.7%
1.1%
1.3%
1.5%
0.6%
0.7%
1.3%
1.4%
1.6%
0.6%
0.7%
1.3%
1.4%
1.6%
0.6%
0.7%
1.3%
1.4%
1.7%
0.6%
0.8%
1.3%
1.4%
1.7%
0.6%
0.8%
1.4%
1.4%
1.8%
0.6%
0.8%
1.4%
Table 6: Change in Average Annual Surface Runoff over Time.
Surface
Runoff
Base
2010
Percent Change in Surface Runoff from Base
2020
2030
2040
2050
2060
2070
2080
2090
2100
Subwatershed #340 (Below Strontia Springs Reservoir)
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
119.8mm
118.3mm
118.8mm
118.1 mm
118.7mm
4.3%
3.8%
2.8%
2.5%
3.2%
8.1%
8.2%
3.8%
3.7%
6.3%
10.2%
10.8%
4.6%
4.8%
9.1%
11.3%
12.1%
5.0%
5.3%
10.6%
11.9%
13.0%
5.4%
5.8%
11.6%
12.5%
14.2%
5.6%
6.1%
11.9%
13.0%
15.7%
5.9%
6.6%
12.6%
13.3%
16.9%
6.2%
7.0%
13.4%
13.4%
17.6%
6.2%
7.4%
14.8%
Watershed Average
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
90.8mm
90.6mm
90.7mm
90.6mm
90.7mm
0.8%
0.7%
0.5%
0.5%
0.6%
1.4%
1.3%
0.7%
0.7%
1.1%
1.7%
1.7%
0.8%
0.8%
1.4%
1.8%
1.9%
0.8%
0.9%
1.6%
1.9%
2.1%
0.9%
0.9%
1.7%
2.0%
2.2%
0.9%
1.0%
1.8%
2.1%
2.4%
0.9%
1.0%
1.8%
2.1%
2.6%
1.0%
1.1%
1.9%
2.1%
2.7%
1.0%
1.1%
2.0%
33
-------
Table 7: Change in Channel Average Annual Sediment Yield over Time.
Sediment
Yield Base
2010
Percent Change in Sediment Yield from Base
2020
2030
2040
2050
2060
2070
2080
2090
2100
Subwatershed #340 Outlet
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
18080 t
17840 t
17920 t
178101
17950 t
2.8%
2.9%
1.8%
2.5%
2.5%
6.6%
6.6%
2.7%
3.3%
4.7%
8.6%
9.3%
3.1%
3.5%
7.5%
9.6%
10.7%
3.7%
3.9%
8.8%
10.0%
11.4%
4.0%
4.4%
9.5%
10.5%
12.2%
4.3%
4.8%
10.1%
11.0%
13.7%
4.5%
5.2%
10.7%
11.1%
14.5%
4.7%
5.6%
11.3%
11.2%
15.1%
4.9%
6.0%
12.8%
Watershed Outlet
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
70740 t
70660 t
70660 t
70620 t
70670 t
0.6%
0.5%
0.4%
0.4%
0.5%
1.0%
0.8%
0.5%
0.5%
0.7%
1.2%
1.2%
0.6%
0.6%
1.1%
1.5%
1.5%
0.7%
0.7%
1.3%
1.6%
1.7%
0.6%
0.7%
1.3%
1.7%
1.9%
0.6%
0.7%
1.5%
1.8%
2.2%
0.6%
0.7%
1.5%
1.8%
2.4%
0.7%
0.7%
1.7%
1.8%
2.7%
0.7%
0.8%
1.9%
34
-------
Appendix D
Table 8:
Land Cover Change for Scenario Al from Baseline 2010 to 2100 (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in
parentheses are the percent change in cover type from the 2010 base case).
Scenario Al
Land Cover Type
Open Water
Perennial Ice/Snow
Developed, Open Space
Developed, Low Intensity
Developed, Medium
Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous
Wetlands
Base (km2)
2010
335.9
234.8
1469.6
4784.1
1142.3
913.5
426.6
428.9
7012.1
27.8
2326.8
27318.6
522.7
13896.2
531.7
333.5
Change from Base (km2)
2020
-4.7
(-1.4%)
0
(0%)
-37.3
(-2.5%)
297.6
(6.22%)
251.6
(22.0%)
150.9
(16.5%)
-2.6
(-0.6%)
-11.1
(-2.6%)
-95.5
(-1.4%)
-0.6
(-2.1%)
-62.7
(-2.7%)
-187.7
(-0.7%)
-32.2
(-6.2%)
-240.8
(-1.7%)
-15.4
(-2.9%)
-9.6
(-2.9%)
2030
-7.0
(-2.1%)
-0.1
(0%)
-56.5
(-3.8%)
379.9
(7.9%)
480.6
(42.1%)
297.7
(32.6%)
-4.6
(-1.1%)
-18.7
(-4.4%)
-142.2
(-2.0%)
-0.8
(-2.7%)
-106.1
(-4.6%)
-330.6
(-1.2%)
-48.9
(-9.4%)
-405.5
(-2.9%)
-22.7
(-4.3%)
-14.9
(-4.5%)
2040
-8.3
(-2.5%)
-0.1
(0%)
-66.9
(-4.6%)
460.2
(9.6%)
536.4
(47.0%)
424.3
(46.5%)
-5.8
(-1.4%)
-22.7
(-5.3%)
-163.2
(-2.3%)
-0.8
(-2.9%)
-127.1
(-5.5%)
-413.7
(-1.5%)
-57.2
(-11.0%)
-510.5
(-3.7%)
-26.6
(-5.0%)
-18.0
(-5.4%)
2050
-8.8
(-2.6%)
-0.1
(0%)
-70.4
(-4.8%)
427.7
(8.9%)
550.4
(48.2%)
531.7
(58.2%)
-6.2
(-1.5%)
-23.7
(-5.5%)
-172.6
(-2.5%)
-0.9
(-3.1%)
-132.7
(-5.7%)
-437.1
(-1.6%)
-60.0
(-11.5%)
-551.1
(-4.0%)
-27.6
(-5.2%)
-18.7
(-5.6%)
2060
-8.9
(-2.6%)
-0.1
(0%)
-71.4
(-4.9%)
392.1
(8.2%)
511.3
(44.8%)
634.1
(69.4%)
-6.4
(-1.5%)
-24.6
(-5.7%)
-177.8
(-2.5%)
-0.9
(-3.2%)
-134.1
(-5.8%)
-448.0
(-1.6%)
-60.7
(-11.6%)
-557.5
(-4.0%)
-28.2
(-5.3%)
-18.9
(-5.7%)
2070
-8.9
(-2.6%)
-0.1
(0%)
-71.4
(-4.9%)
354.6
(7.4%)
441.2
(38.6%)
744.5
(81.5%)
-6.4
(-1.5%)
-24.6
(-5.7%)
-178.7
(-2.6%)
-0.9
(-3.3%)
-134.8
(-5.8%)
-448.8
(-1.6%)
-60.7
(-11.6%)
-558.0
(-4.0%)
-28.2
(-5.3%)
-18.9
(-5.7%)
2080
-8.9
(-2.6%)
-0.1
(0%)
-71.5
(-4.9%)
321.7
(6.7%)
369.7
(32.4%)
849.6
(93.0%)
-6.4
(-1.5%)
-24.6
(-5.7%)
-178.9
(-2.6%)
-0.9
(-3.3%)
-134.9
(-5.8%)
-449.0
(-1.6%)
-60.7
(-11.6%)
-558.0
(-4.0%)
-28.2
(-5.3%)
-18.9
(-5.7%)
2090
-8.9
(-2.6%)
-0.1
(0%)
-71.6
(-4.9%)
297.7
(6.2%)
360.2
(31.5%)
884.2
(96.8%)
-6.4
(-1.5%)
-24.6
(-5.7%)
-178.9
(-2.6%)
-0.9
(-3.3%)
-134.9
(-5.8%)
-449.1
(-1.6%)
-60.7
(-11.6%)
-558.7
(-4.0%)
-28.3
(-5.3%)
-18.9
(-5.7%)
2100
-8.9
(-2.6%)
-0.1
(0%)
-71.7
(-4.9%)
284.6
(6.0%)
351.9
(30.8%)
912.3
(99.9%)
-6.4
(-1.5%)
-25.5
(-6.0%)
-180.1
(-2.6%)
-1.0
(-3.4%)
-135.1
(-5.8%)
-452.9
(-1.7%)
-60.7
(-11.6%)
-558.8
(-4.0%)
-28.5
(-5.4%)
-19.2
(-5.8%)
35
-------
Table 9:
Land Cover Change for Scenario A2 from Baseline 2010 to 2100 (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in
parentheses are the percent change in cover type from the 2010 base case).
Scenario A2
Land Cover Type
Open Water
Perennial Ice/Snow
Developed, Open Space
Developed, Low Intensity
Developed, Medium
Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous
Wetlands
Base (km2)
2010
337.7
234.8
1484.4
4666.7
1083.1
879.6
427.1
431.9
7028.6
28.0
2344.8
27376.5
532.3
13976.0
536.7
336.7
Change from Base (km2)
2020
-4.7
(-1.4%)
0
(0%)
-36.9
(-2.5%)
388.8
(8.3%)
163.3
(15.1%)
91.9
(10.4%)
-2.0
(-0.5%)
-10.9
(-2.5%)
-90.8
(-1.3%)
-0.6
(-2.3%)
-60.1
(-2.6%)
-175.2
(-0.6%)
-29.2
(-5.5%)
-209.0
(-1.5%)
-15.2
(-2.8%)
-9.3
(-2.8%)
2030
-7.7
(-2.3%)
0
(0%)
-58.8
(-4.0%)
407.4
(8.7%)
539.4
(49.8%)
193.1
(22.0%)
-3.9
(-0.9%)
-19.0
(-4.4%)
-146.1
(-2.1%)
-0.9
(-3.1%)
-108.4
(-4.6%)
-320.8
(-1.2%)
-50.3
(-9.5%)
-384.9
(-2.8%)
-24.0
(-4.5%)
-15.1
(-4.5%)
2040
-9.9
(-2.9%)
-0.1
(0%)
-74.6
(-5.0%)
544.3
(11.7%)
667.8
(61.7%)
293.0
(33.3%)
-5.2
(-1.2%)
T3 O
-23.8
(-5.5%)
-177.5
(-2.5%)
-1.0
(-3.6%)
-138.9
(-5.9%)
-423.2
(-1.6%)
-62.9
(-11.8%)
-538.8
(-3.9%)
-29.7
(-5.5%)
-19.6
(-5.8%)
2050
-11.0
(-3.3%)
-0.1
(0%)
-82.8
(-5.6%)
598.5
(12.8%)
713.0
(65.8%)
401.9
(45.7%)
-6.0
(-1.4%)
-25.8
(-6.0%)
-190.7
(-2.7%)
-1.0
(-3.7%)
-153.7
(-6.6%)
-476.3
(-1.7%)
-70.4
(-13.2%)
-641.7
(-4.6%)
-32.3
(-6.0%)
-21.6
(-6.4%)
2060
-12.0
(-3.6%)
-0.1
(0%)
-88.2
(-5.9%)
571.7
(12.3%)
746.9
(69.0%)
516.6
(58.7%)
-6.5
(-1.5%)
-26.8
(-6.2%)
-202.4
(-2.9%)
-1.1
(-4.0%)
-160.4
(-6.8%)
-501.9
(-1.8%)
-74.8
(-14.1%)
-704.7
(-5.0%)
-33.6
(-6.3%)
-22.8
(-6.8%)
2070
-12.6
(-3.7%)
-0.1
(0%)
-91.1
(-6.1%)
517.1
(11.1%)
698.8
(64.5%)
697.1
(79.2%)
-6.8
(-1.6%)
-27.4
(-6.4%)
-208.2
(-3.0%)
-1.1
(-4.0%)
-164.6
(-7.0%)
-520.0
(-1.9%)
-77.8
(-14.6%)
-745.1
(-5.3%)
-34.6
(-6.4%)
-23.7
(-7.0%)
2080
-13.2
(-3.9%)
-0.1
(0%)
-93.9
(-6.3%)
467.3
(10.0%)
534.6
(49.4%)
985.3
(112.0%)
-7.0
(-1.6%)
-27.7
(-6.4%)
-211.5
(-3.0%)
-1.1
(-4.1%)
-166.9
(-7.1%)
-536.1
(-2.0%)
-81.0
(-15.2%)
-789.2
(-5.7%)
-35.3
(-6.6%)
-24.4
(-7.3%)
2090
-13.7
(-4.1%)
-0.1
(0%)
-96.7
(-6.5%)
406.9
(8.7%)
421.9
(39.0%)
1232.0
(140.1%)
-7.1
(-1.7%)
-28.3
(-6.6%)
-217.2
(-3.1%)
-1.2
(-4.4%)
-169.2
(-7.2%)
-551.8
(-2.0%)
-84.1
(-15.8%)
-830.1
(-5.9%)
-36.1
(-6.7%)
-25.2
(-7.5%)
2100
-14.3
(-4.2%)
-0.1
(0%)
-100.4
(-6.8%)
364.6
(7.8%)
376.2
(34.7%)
1409.0
(160.2%)
-7.4
(-1.7%)
-28.8
(-6.7%)
-222.5
(-3.2%)
-1.2
(-4.4%)
-172.3
(-7.4%)
-567.8
(-2.1%)
-87.8
(-16.5%)
-883.5
(-6.3%)
-37.4
(-7.0%)
-26.3
(-7.8%)
36
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Table 10: Land Cover Change for Scenario Bl from Baseline 2010 to 2100 (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in
parentheses are the percent change in cover type from the 2010 base case).
Scenario Bl
Land Cover Type
Open Water
Perennial Ice/Snow
Developed, Open Space
Developed, Low Intensity
Developed, Medium
Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous
Wetlands
Base (km2)
2010
336.8
234.8
1473.7
4691.9
1110.8
906.2
426.9
432.1
7027.7
27.9
2341.6
27360.6
528.5
13935.3
534.6
335.6
Change from Base (km2)
2020
-3.4
(-1.0%)
0
(0%)
-26.0
(-1.8%)
256.6
(5.5%)
75.9
(6.8%)
124.7
(13.8%)
-1.5
(-0.4%)
-7.1
(-1.6%)
-55.2
(-0.8%)
-0.4
(-1.3%)
-37.2
(-1.6%)
-125.4
(-0.5%)
-19.7
(-3.7%)
-165.3
(-1.2%)
-10.1
(-1.9%)
-6.1
(-1.8%)
2030
-4.5
(-1.3%)
0
(0%)
-35.2
(-2.4%)
326.9
(7.0%)
94.4
(8.5%)
223.2
(24.6%)
-2.2
(-0.5%)
-9.9
(-2.3%)
-76.3
(-1.1%)
-0.4
(-1.6%)
-54.0
(-2.3%)
-177.2
(-0.7%)
-27.1
(-5.1%)
-234.8
(-1.7%)
-14.2
(-2.7%)
-8.7
(-2.6%)
2040
-5.0
(-1.5%)
0
(0%)
-39.0
(-2.7%)
274.9
(5.9%)
140.4
(12.6%)
299.2
(33.0%)
-2.5
(-0.6%)
-10.7
(-2.5%)
-79.9
(-1.1%)
-0.4
(-1.6%)
-61.2
(-2.6%)
-196.5
(-0.7%)
-30.8
(-5.8%)
-263.3
(-1.9%)
-15.3
(-2.9%)
-9.6
(-2.9%)
2050
-5.1
(-1.5%)
0
(0%)
-39.4
(-2.7%)
199.9
(4.3%)
148.7
(13.4%)
372.3
(41.1%)
-2.6
(-0.6%)
-10.8
(-2.5%)
-80.0
(-1.1%)
-0.4
(-1.6%)
-61.6
(-2.6%)
-198.1
(-0.7%)
-31.1
(-5.9%)
-266.7
(-1.9%)
-15.4
(-2.9%)
-9.7
(-2.9%)
2060
-5.1
(-1.5%)
0
(0%)
-39.5
(-2.7%)
161.2
(3.4%)
107.8
(9.7%)
453.0
(50.0%)
-2.6
(-0.6%)
-10.9
(-2.5%)
-80.0
(-1.1%)
-0.4
(-1.6%)
-61.8
(-2.6%)
-198.8
(-0.7%)
-31.1
(-5.9%)
-266.9
(-1.9%)
-15.4
(-2.9%)
-9.7
(-2.9%)
2070
-5.1
(-1.5%)
0
(0%)
-39.5
(-2.7%)
127.8
(2.7%)
67.4
(6.1%)
526.9
(58.1%)
-2.6
(-0.6%)
-10.9
(-2.5%)
-80.0
(-1.1%)
-0.4
(-1.6%)
-61.8
(-2.6%)
-198.8
(-0.7%)
-31.1
(-5.9%)
-266.9
(-1.9%)
-15.4
(-2.9%)
-9.7
(-2.9%)
2080
-5.1
(-1.5%)
0
(0%)
-39.5
(-2.7%)
101.6
(2.2%)
18.5
(1.7%)
601.9
(66.4%)
-2.6
(-0.6%)
-10.9
(-2.5%)
-80.0
(-1.1%)
-0.4
(-1.6%)
-61.8
(-2.6%)
-198.8
(-0.7%)
-31.1
(-5.9%)
-266.9
(-1.9%)
-15.4
(-2.9%)
-9.7
(-2.9%)
2090
-5.1
(-1.5%)
0
(0%)
-39.5
(-2.7%)
75.8
(1.6%)
-16.9
(-1.5%)
663.1
(73.2%)
-2.6
(-0.6%)
-10.9
(-2.5%)
-80.0
(-1.1%)
-0.4
(-1.6%)
-61.8
(-2.6%)
-198.8
(-0.7%)
-31.1
(-5.9%)
-266.9
(-1.9%)
-15.4
(-2.9%)
-9.7
(-2.9%)
2100
-5.1
(-1.5%)
0
(0%)
-39.5
(-2.7%)
56.0
(1.2%)
-43.3
(-3.9%)
709.3
(78.3%)
-2.6
(-0.6%)
-10.9
(-2.5%)
-80.0
(-1.1%)
-0.4
(-1.6%)
-61.8
(-2.6%)
-198.8
(-0.7%)
-31.1
(-5.9%)
-266.9
(-1.9%)
-15.4
(-2.9%)
-9.7
(-2.9%)
37
-------
Table 11: Land Cover Change for Scenario B2 from Baseline 2010 to 2100 (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in parentheses are
the percent change in cover type from the 2010 base case).
Scenario B2
Land Cover Type
Open Water
Perennial Ice/Snow
Developed, Open Space
Developed, Low Intensity
Developed, Medium
Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous
Wetlands
Base (km2)
2010
337.9
234.8
1484.8
4618.3
1085.5
881.1
427.7
433.5
7038.9
28.0
2351.1
27394.7
534.3
13978.7
538.1
337.5
Change from Base (km2)
2020
-3.9
(-1.1%)
0
(0%)
-28.6
(-1.9%)
284.5
(6.2%)
90.4
(8.3%)
83.3
(9.5%)
-1.8
(-0.4%)
-7.3
(-1.7%)
-57.1
(-0.8%)
-0.4
(-1.3%)
-36.6
(-1.6%)
-127.2
(-0.5%)
-20.2
(-3.8%)
-157.4
(-1.1%)
-11.4
(-2.1%)
-6.4
(-1.9%)
2030
-5.2
(-1.5%)
0
(0%)
-41.9
(-2.8%)
398.8
(8.6%)
122.8
(11.3%)
165.4
(18.8%)
-2.6
(-0.6%)
-10.3
(-2.4%)
-82.1
(-1.2%)
-0.5
(-1.7%)
-58.0
(-2.5%)
-192.3
(-0.7%)
-29.4
(-5.5%)
-239.4
(-1.7%)
-16.0
(-3.0%)
-9.3
(-2.8%)
2040
-5.9
(-1.7%)
0
(0%)
-47.6
(-3.2%)
365.6
(7.9%)
196.1
(18.1%)
238.4
(27.1%)
-2.9
(-0.7%)
-11.6
(-2.7%)
-88.0
(-1.3%)
-0.5
(-1.8%)
-67.4
(-2.9%)
-220.0
(-0.8%)
-35.0
(-6.5%)
-292.3
(-2.1%)
-18.1
(-3.4%)
-10.9
(-3.2%)
2050
-6.5
(-1.9%)
0
(0%)
-50.4
(-3.4%)
326.4
(7.1%)
232.2
(21.4%)
298.7
(33.9%)
-3.3
(-0.8%)
-12.0
(-2.8%)
-89.4
(-1.3%)
-0.5
(-1.8%)
-71.7
(-3.1%)
-232.6
(-0.9%)
-38.5
(-7.2%)
-322.1
(-2.3%)
-18.9
(-3.5%)
-11.6
(-3.4%)
2060
-6.6
(-2.0%)
0
(0%)
-52.1
(-3.5%)
261.9
(5.7%)
254.5
(23.5%)
362.6
(41.2%)
-3.3
(-0.8%)
-12.2
(-2.8%)
-90.0
(-1.3%)
-0.5
(-1.8%)
-73.2
(-3.1%)
-237.4
(-0.9%)
-39.8
(-7.5%)
-332.7
(-2.4%)
-19.3
(-3.6%)
-11.8
(-3.5%)
2070
-6.7
(-2.0%)
0
(0%)
-52.8
(-3.6%)
202.9
(4.4%)
235.7
(21.7%)
448.9
(50.9%)
-3.3
(-0.8%)
-12.3
(-2.8%)
-90.2
(-1.3%)
-0.5
(-1.8%)
-74.1
(-3.2%)
-240.5
(-0.9%)
-40.0
(-7.5%)
-335.7
(-2.4%)
-19.3
(-3.6%)
-11.9
(-3.5%)
2080
-6.7
(-2.0%)
0
(0%)
-53.3
(-3.6%)
148.8
(3.2%)
195.8
(18.0%)
547.3
(62.1%)
-3.4
(-0.8%)
-12.3
(-2.9%)
-90.5
(-1.3%)
-0.5
(-1.8%)
-74.6
(-3.2%)
-242.0
(-0.9%)
-40.0
(-7.5%)
-337.3
(-2.4%)
-19.4
(-3.6%)
-11.9
(-3.5%)
2090
-6.7
(-2.0%)
0
(0%)
-53.5
(-3.6%)
97.6
(2.1%)
127.7
(11.8%)
669.3
(76.0%)
-3.4
(-0.8%)
-12.4
(-2.9%)
-90.9
(-1.3%)
-0.5
(-1.8%)
-74.9
(-3.2%)
-242.9
(-0.9%)
-40.1
(-7.5%)
-338.1
(-2.4%)
-19.4
(-3.6%)
-11.9
(-3.5%)
2100
-6.7
(-2.0%)
0
(0%)
-53.5
(-3.6%)
48.0
(1.0%)
69.9
(6.4%)
777.4
(88.2%)
-3.4
(-0.8%)
-12.4
(-2.9%)
-91.5
(-1.3%)
-0.5
(-1.8%)
-74.9
(-3.2%)
-243.0
(-0.9%)
-40.1
(-7.5%)
-338.1
(-2.4%)
-19.4
(-3.6%)
-11.9
(-3.5%)
38
-------
Table 12: Land Cover Change for Baseline BC from Baseline 2010 to 2100 (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in
parentheses are the percent change in cover type from the 2010 base case).
Scenario BC
Land Cover Type
Open Water
Perennial Ice/Snow
Developed, Open Space
Developed, Low Intensity
Developed, Medium
Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous
Wetlands
Base (km2)
2010
337.2
234.8
1480.0
4710.6
1092.0
882.9
427.0
431.0
7025.5
27.9
2341.5
27364.4
529.7
13948.7
535.8
335.9
Change from Base (km2)
2020
-4.6
(-1.4%)
0
(0%)
-34.2
(-2.3%)
407.3
(8.7%)
111.5
(10.2%)
90.2
(10.2%)
-1.9
(-0.4%)
-9.7
(-2.3%)
-81.1
(-1.2%)
-0.6
(-2.2%)
-56.0
(-2.4%)
-160.7
(-0.6%)
-28.7
(-5.4%)
-207.9
(-1.5%)
-14.8
(-2.8%)
-8.9
(-2.7%)
2030
-6.9
(-2.0%)
0
(0%)
-53.8
(-3.6%)
500.6
(10.6%)
342.0
(31.3%)
181.2
(20.5%)
-3.5
(-0.8%)
-15.8
(-3.7%)
-122.3
(-1.7%)
-0.8
(-2.8%)
-95.9
(-4.1%)
-278.1
(-1.0%)
-47.1
(-8.9%)
-364.1
(-2.6%)
-22.1
(-4.1%)
-13.5
(-4.0%)
2040
-8.9
(-2.6%)
0
(0%)
-68.0
(-4.6%)
524.1
(11.1%)
546.0
(50.0%)
264.2
(29.9%)
-4.8
(-1.1%)
-19.6
(-4.5%)
-145.0
(-2.1%)
-0.9
(-3.1%)
-122.5
(-5.2%)
-359.7
(-1.3%)
-58.9
(-11.1%)
-502.2
(-3.6%)
-26.9
(-5.0%)
-16.9
(-5.0%)
2050
-10.1
(-3.0%)
0
(0%)
-75.2
(-5.2%)
544.0
(11.6%)
617.7
(56.6%)
339.9
(38.5%)
-5.8
(-1.4%)
-21.6
(-5.0%)
-153.3
(-2.2%)
-0.9
(-3.1%)
-134.9
(-5.8%)
-400.2
(-1.5%)
-65.2
(-12.3%)
-587.2
(-4.2%)
-28.8
(-5.4%)
-18.6
(-5.5%)
2060
-10.7
(-3.2%)
0
(0%)
-78.6
(-5.3%)
534.4
(11.3%)
632.3
(57.9%)
415.5
(47.1%)
-6.0
(-1.4%)
-22.4
(-5.2%)
-156.5
(-2.2%)
-0.9
(-3.2%)
-139.2
(-5.9%)
-419.2
(-1.5%)
-67.8
(-12.8%)
-631.9
(-4.5%)
-29.6
(-5.5%)
-19.4
(-5.8%)
2070
-11.0
(-3.7%)
0
(0%)
-80.4
(-5.4%)
491.4
(10.4%)
624.7
(57.2%)
510.9
(57.9%)
-6.1
(-1.4%)
-22.6
(-5.3%)
-157.6
(-2.2%)
-0.9
(-3.2%)
-140.5
(-6.0%)
-428.9
(-1.6%)
-69.5
(-13.1%)
-659.6
(-4.7%)
-30.0
(-5.6%)
-19.9
(-5.9%)
2080
-11.2
(-3.3%)
0
(0%)
-81.9
(-5.5%)
433.1
(9.2%)
596.2
(54.6%)
622.9
(70.6%)
-6.1
(-1.4%)
-22.8
(-5.3%)
-158.2
(-2.3%)
-0.9
(-3.2%)
-141.3
(-6.0%)
-435.1
(-1.6%)
-70.3
(-13.3%)
-673.9
(-4.8%)
-30.3
(-5.7%)
-20.1
(-6.0%)
2090
-11.3
(-3.4%)
0
(0%)
-82.5
(-5.6%)
369.9
(7.9%)
518.5
(47.5%)
776.6
(88.0%)
-6.2
(-1.4%)
-22.9
(-5.3%)
-158.6
(-2.3%)
-0.9
(-3.2%)
-141.5
(-6.0%)
-437.9
(-1.6%)
-71.1
(-13.4%)
-681.5
(-4.9%)
-30.4
(-5.7%)
-20.2
(-6.0%)
2100
-11.4
(-3.4%)
0
(0%)
-82.5
(-5.6%)
301.0
(6.4%)
350.7
(32.1%)
1016.2
(115.1%)
-6.2
(-1.5%)
-22.9
(-5.3%)
-158.8
(-2.3%)
-0.9
(-3.2%)
-141.6
(-6.1%)
-438.8
(-1.6%)
-71.3
(-13.5%)
-682.9
(-4.9%)
-30.4
(-5.7%)
-20.2
(-6.0%)
39
-------
40
-------
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